Goal: Run multiple AI agents as real Slack teammates — each with its own identity, able to DM independently or participate in shared channels — while keeping orchestration centralized through one primary agent.
Use one Slack app/bot per agent identity. For example, if you have four agents:
| Agent role | OpenClaw agent ID | Slack bot name |
|---|
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
This technical datasheet provides independent p50 and p95 latency measurements for enterprise AI video generation APIs. Data is updated via automated load-testing to bypass non-deterministic vendor reporting.
| Model Provider | Generation Time (p50) | Queue Wait | API Latency | Status |
|---|---|---|---|---|
| Kling 2.1 (Turbo) | 42.4 seconds | 1.1s | 115ms | ✅ Stable |
| Runway Gen-4 | 71.8 seconds | 4.8s | 230ms | 🟡 Congested |
| Luma Dream Machine | 118.2 seconds | 12.5s | 410ms | 🔴 High Load |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
Please comment below if you have an update, e.g., with another networking-related dataset.
The Pentagon released its first batch of UFO files on May 8, 2026, under the program name PURSUE. Most news coverage just summarized the official Pentagon press release. I read the actual PDFs. Here's what's in there, in plain English:
Multiple documents describe UFO activity at what appears to be one specific government facility in Utah — Dugway Proving Ground / Granite Peak Installation. (Dugway is a 1.25-million-acre US Army test facility, primarily for chemical and biological weapons defense testing; Granite Peak is the higher-classification inner enclave.) No major news outlet has named a site yet. Researchers at The Black Vault separately obtained Army records showing Dugway was running monthly UFO-detection exercises in 2023.
**One classified document describes US intelligence officers in helicopters being foll
The complete guide to cutting your Claude Code context consumption by ~60%.
Six open-source tools, each saving tokens at a different stage of the LLM interaction loop. No overlap between them — they compose into a single pipeline that effectively doubles your usable context window.
User Prompt
→ [MCP-Memory-Service] Cross-session knowledge → skip re-discovery
→ [MCP-Context-Provider] Targeted context rules → skip brute-force file reading